This chapter presents an overview of AI methods for playing games. The main approaches discussed are planning, reinforcement learning, and supervised learning. There is also a brief discussion of behavior authoring, hybrid methods, and machine learning methods that learn game representations and forward models rather than policies. Throughout the chapter, there are multiple examples of these approaches from the scientific literature and published games.